A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

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Background: Small non-coding RNAs (sRNAs) are regulatory molecules, present in all forms of life, known to regulate various biological processes in response to the different environmental signals. In recent years, deep sequencing and various other computational prediction methods have been employed to identify and analyze sRNAs. Results: In the present study, we have applied an improved sRNA scanner method to predict sRNAs from the genome of Rhizobium etli, based on PWM matrix of conditional sigma factor 32. sRNAs predicted from the genome are integrated with the available stress specific transcriptome data to predict putative conditional specific sRNAs. A total of 271 sRNAs from the genome and 173 sRNAs from the transcriptome are computationally predicted. Of these, 25 sRNAs are found in both genome and transcriptome data. Putative targets for these sRNAs are predicted using TargetRNA2 and these targets are involved in a wide array of cellular functions such as cell division, transport and metabolism of amino acids, carbohydrates, energy production and conversion, translation, cell wall/membrane biogenesis, posttranslation modification, protein turnover and chaperones. Predicted targets are functionally classified based on COG analysis and GO annotations. Conclusion:sRNAs predicted from the genome, using PWM matrices for conditional sigma factor 32 could be a better method to identify the conditional specific sRNAs which expand the list of putative sRNAs from the intergenic regions (IgRs) of R. etli and closely related α-proteobacteria. sRNAs identified in this study would be helpful to explore their regulatory role in biological cellular process during the stress.

Plant lectins are the heterogenous group of glycoproteins extensively studied for their potent insecticidal property against Hemipteran pests. In this present study, the full-length cDNA of monocot mannose-binding insecticidal lectin gene was isolated from Allium ascalonicum leaves. The isolated Allium ascalonicum Lectin (AAL) gene was cloned in pGEM-T vector, sequenced and the sequence was submitted to GenBank (KM096570.1). Sequence analysis revealed a 468 bp open reading frame (ORF) encoding a putative 155 amino acids agglutinin precursor. Multiple sequence alignment and phylogenetic analysis of AAL amino acid with those of 30 other Mannose binding lectin (MBL) sequences in NCBI revealed a high similarity of 85-95% indicating that AAL is a member of the MBL super family and forms a cluster with other onion lectins. Secondary structure prediction and the homology modeling showed that AAL protein possess predominantly β-sheets and three potential mannose-binding motifs consisting of 5 amino acid residues QDNVY like other GNA lectins. The results of the insilico analysis predict that the Allium ascalonicum lectin gene can be another potent insecticidal protein.